def test_wera_radial_to_netcdf():
radial_file = data_path / 'radials' / 'WERA' / 'RDL_csw_2019_10_24_162300.ruv'
nc_file = output_path / 'radials_nc' / 'WERA' / 'RDL_csw_2019_10_24_162300.nc'
# Converts the underlying .data (natively a pandas DataFrame)
# to an xarray object when `create_netcdf` is called.
# This automatically 'enhances' the netCDF file
# with better variable names and attributes.
rad1 = Radial(radial_file)
rad1.export(str(nc_file), file_type='netcdf')
# Convert it to an xarray Dataset with no variable
# or attribte enhancements
xds2 = rad1.to_xarray(enhance=False)
# Convert it to xarray Dataset with increased usability
# by changing variables names, adding attributes,
# and decoding the CF standards like scale_factor
xds3 = rad1.to_xarray(enhance=True)
with xr.open_dataset(nc_file) as xds1:
# The two enhanced files should be identical
assert xds1.identical(xds3)
# Enhanced and non-enhanced files should not
# be equal
assert not xds1.identical(xds2)
def test_wera_raw_to_quality_nc():
radial_file = data_path / 'radials' / 'WERA' / 'RDL_csw_2019_10_24_162300.ruv'
nc_file = output_path / 'radials_qc_nc' / 'WERA' / 'RDL_csw_2019_10_24_162300.nc'
rad1 = Radial(radial_file, mask_over_land=False, replace_invalid=False)
rad1.mask_over_land()
rad1.qc_qartod_radial_count()
rad1.qc_qartod_valid_location()
rad1.qc_qartod_maximum_velocity()
rad1.qc_qartod_spatial_median()
rad1.export(str(nc_file), file_type='netcdf')
xds2 = rad1.to_xarray(enhance=True)
with xr.open_dataset(nc_file) as xds1:
assert len(xds1.QCTest) == 3 # no VFLG column so one test not run
# The two enhanced files should be identical
assert xds1.identical(xds2)
